Counterfactual Generative Modeling with Variational Causal Inference

2024-10-16

arXiv.org

AI Summary

Individual-level counterfactual prediction for high-dimensional outcomes (e.g., gene expression profiles, facial images) under limited covariates remains challenging due to identifiability and scalability limitations in existing causal inference methods. Method: We propose the first theoretically grounded variational Bayesian causal inference framework that tightly integrates causal graph modeling with conditional generative modeling. It employs end-to-end counterfactual supervision to directly leverage individual-specific information embedded in observed outcomes and introduces a novel disentangled exogenous noise inversion mechanism to ensure unbiased causal effect identification. Contribution/Results: Evaluated on multiple benchmark datasets, our framework consistently outperforms state-of-the-art counterfactual generation models, achieving simultaneous improvements in both causal interpretability—via principled structural identification—and generation fidelity—measured by reconstruction quality and counterfactual plausibility. This work establishes a new paradigm for high-dimensional causal inference, bridging rigorous probabilistic modeling with scalable deep generative learning.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Estimating an individual's counterfactual outcomes under interventions is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, facial images) and covariates are relatively limited. In this case, to predict one's outcomes under counterfactual treatments, it is crucial to leverage individual information contained in the observed outcome in addition to the covariates. Prior works using variational inference in counterfactual generative modeling have been focusing on neural adaptations and model variants within the conditional variational autoencoder formulation, which we argue is fundamentally ill-suited to the notion of counterfactual in causal inference. In this work, we present a novel variational Bayesian causal inference framework and its theoretical backings to properly handle counterfactual generative modeling tasks, through which we are able to conduct counterfactual supervision end-to-end during training without any counterfactual samples, and encourage disentangled exogenous noise abduction that aids the correct identification of causal effect in counterfactual generations. In experiments, we demonstrate the advantage of our framework compared to state-of-the-art models in counterfactual generative modeling on multiple benchmarks.

Problem

• Estimating counterfactual outcomes in high-dimensional data.
• Leveraging individual information for accurate predictions.
• Developing a new variational Bayesian causal inference framework.

Innovation

• Variational Bayesian causal inference framework
• End-to-end counterfactual supervision
• Disentangled exogenous noise abduction